It is generally accepted that equalization of a single carrier signal is done by an adaptive equalizer. The adaptive equalization can be done on the data as in QAM (Quadrature Amplitude Modulation) based signals or on combination of a special training signal and the data as in VSB (Vestigial Side Band). The equalizer is normally configured in an IIR mode (An FIR plus a feedback equalizer) to provide coverage for long delayed multipath with a minimal number of taps, thus hardware efficient. Learning of the taps values of the equalizing filter is done with the LMS algorithm or some of its variations.
The LMS algorithm is usually applied because of its limited complexity. It is not however optimal in term of channel correction capability. The LMS is a continuous learning and tracking process that does not provide any means of control on the taps value build up. It works excellently in simple conditions, normally found when there is a line of sight to the transmission tower and when an outdoor antenna is used. In many other conditions that are common for TV reception like: indoor antenna use, downtown area surrounded with tall buildings or hilly terrain, the adaptive IIR equalizer with LMS learning algorithm does not always work. Such conditions normally induce short delays which pose difficulty to LMS based algorithms to cope with.
The ATSC standard (Advanced TV System Community), for the DTV transmission (Digital TV) in the USA, is based on VSB modulation that includes a reference signal (referred to also as training signal). The reference signal substitutes the data every 313 segments (312 segments of data and one segment of reference information). The reference signal is made of a training pattern and additional data, using only 2 levels constellations (M=2). The reference pattern assists in the LMS convergence rate. The training pattern is designed for the LMS based algorithms.
A typical VSB technology by an LMS algorithm adapts an equalizer filter taps b, according to the following equations:bn=bn−1+μn*en*xn     bn—The equalizer taps vector at time n.    bn−1—The equalizer taps vector at time n−1.    xn—The received signal.    en—The present equalization error.    μn—The algorithm step size, which sets the convergence rate of bn to the optimal value bopt. In order to ensure stable convergence process, μn has to be limited.
Adaptive equalization based on the LMS algorithm suffers from the following disadvantages:    1. Low tracking capability of time varying channel, due to stability condition on μ.    2. Limited performance in present of non line of sight (LOS) channel conditions. The C/N penalty of typical ATSC demodulators increase significantly for Rayleigh channel conditions (low C/E level).    3. Stability loss of the LMS IIR section, in presence of severe indoor conditions.